Selecting an optimal model for forecasting the volumes of railway goods transportation

Consideration was given to selection of an optimal model of short-term forecasting of the volumes of railway transport from the historical and exogenous time series. The historical data carry information about the transportation volumes of various goods between pairs of stations. It was assumed that the result of selecting an optimal model depends on the level of aggregation in the types of goods, departure and destination points, and time. Considered were the models of vector autoregression, integrated model of the autoregressive moving average, and a nonparametric model of histogram forecasting. Criteria for comparison of the forecasts on the basis of distances between the errors of model forecasts were proposed. They are used to analyze the models with the aim of determining the admissible requests for forecast, the actual forecast depth included.

[1]  Jan R. Magnus,et al.  Methodology and Tacit Knowledge: Two Experiments in Econometrics , 1999 .

[2]  P. Whittle.  Prediction and Regulation by Linear Least-Square Methods , 1983 .

[3]  R. Koenker.  Quantile Regression: Name Index , 2005 .

[4]  G. Zhang,et al.  A comparative study of linear and nonlinear models for aggregate retail sales forecasting , 2003 .

[5]  Robert Serfling,et al.  Quantile functions for multivariate analysis: approaches and applications , 2002 .

[6]  W. Härdle,et al.  Applied Nonparametric Regression , 1991 .

[7]  E. Ziegel.  Forecasting and Time Series: An Applied Approach , 2000 .

[8]  David R. Brillinger,et al.  Time Series , 2018, Randomization, Bootstrap and Monte Carlo Methods in Biology.

[9]  Robert L. Winkler,et al.  The accuracy of extrapolation (time series) methods: Results of a forecasting competition , 1982 .

[10]  Linglong Kong,et al.  Quantile tomography: using quantiles with multivariate data , 2008, Statistica Sinica.

[11]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[12]  D. Peña,et al.  Multifold Predictive Validation in ARMAX Time Series Models , 2005 .

[13]  Fang-Mei Tseng,et al.  Applied Hybrid Grey Model to Forecast Seasonal Time Series , 2001 .

[14]  V. Koltchinskii.  M-estimation, convexity and quantiles , 1997 .

[15]  Philip Hans Franses,et al.  The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production , 2001 .

[16]  J. Scott Armstrong,et al.  Beyond Accuracy: Comparison of Criteria Used to Select Forecasting Methods , 1995 .

[17]  P. McSharry,et al.  A comparison of univariate methods for forecasting electricity demand up to a day ahead , 2006 .

[18]  Ching-Wu Chu,et al.  A comparison of univariate methods for forecasting container throughput volumes , 2009, Math. Comput. Model..

[19]  C. Granger,et al.  Co-integration and error correction: representation, estimation and testing , 1987 .

[20]  Chris Chatfield,et al.  Time‐series forecasting , 2000 .

[21]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[22]  Andrew J. Patton,et al.  Properties of Optimal Forecasts under Asymmetric Loss and Nonlinearity , 2007 .

[23]  Mehdi Roopaei,et al.  Economical forecasting by exogenous variables , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[24]  M. M. Stenina,et al.  Forecasting nonstationary time series under asymmetric loss , 2015 .